基于分数阶模型的锂电池SOC估计

State of charge estimation of lithium batteries based on fractional model

为提升锂电池荷电状态(state of charge,SOC)估计的精度,以二阶RC分数阶模型为研究对象,提出一种由分数阶无迹卡尔曼滤波算法和带可变遗忘因子最小二乘法组成的FOUKF+VFFRLS算法。其中分数阶无迹卡尔曼滤波算法用于锂离子电池荷电状态估计,带可变遗忘因子最小二乘法用于电池参数估计。该算法通过对状态变量和参数变量的递推估算,确保了电池状态和参数的实时更新。基于UDDS工况下的实验数据进行仿真分析,结果表明,该方法较FOUKF等算法具有更高的估计精度,电池SOC最大估计误差可控制在2%以内,验证了所提方法的正确性及有效性。

In order to improve the accuracy of estimating the state of charge(SOC)of lithium batteries,taking the second-order RC fractional model as the research object,a FOUKF+VFFRLS algorithm was proposed,which consists of the fractional unscented Kalman filter algorithm and the least squares method with variable forgetting factor.The fractional unscented Kalman filter algorithm was used to estimate the state of charge of lithium-ion batteries,and the least square method with variable forgetting factor was adopted to estimate battery parameters.By recursive estimation of state variables and parameter variables,the algorithm ensured the real-time update of batteries state and parameter.Based on the experimental data under UDDS conditions,the simulation results show that the proposed method has higher estimation accuracy than FOUKF and other algorithms,and the maximum estimation error of battery SOC can be controlled within 2%,which verifies the correctness and effectiveness of the proposed method.